Optional clientOptional collectionOptional filterMethod to add documents to the vector store. It converts the documents into vectors, and adds them to the store.
Array of Document instances.
Optional options: { Optional arguments for adding documents
Optional ids?: string[]Promise that resolves when the documents have been added.
Method to add vectors to the vector store. It converts the vectors into rows and inserts them into the database.
Array of vectors.
Array of Document instances.
Optional options: { Optional arguments for adding documents
Optional ids?: string[]Promise that resolves when the vectors have been added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<PGVectorStore>>Optional filter: MetadataOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to delete documents from the vector store. It deletes the documents that match the provided ids or metadata filter. Matches ids exactly and metadata filter according to postgres jsonb containment. Ids and filter are mutually exclusive.
Object containing either an array of ids or a metadata filter object.
Optional filter?: MetadataOptional ids?: string[]Promise that resolves when the documents have been deleted.
Error if neither ids nor filter are provided, or if both are provided.
Delete by ids
await vectorStore.delete({ ids: ["id1", "id2"] });
Delete by filter
await vectorStore.delete({ filter: { a: 1, b: 2 } });
Optional k: numberOptional filter: MetadataOptional _callbacks: CallbacksMethod to perform a similarity search in the vector store. It returns
the k most similar documents to the query vector, along with their
similarity scores.
Query vector.
Number of most similar documents to return.
Optional filter: MetadataOptional filter to apply to the search.
Promise that resolves with an array of tuples, each containing a Document and its similarity score.
Optional k: numberOptional filter: MetadataOptional _callbacks: CallbacksOptional maxReturn documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Text to look up documents similar to.
Static fromStatic method to create a new PGVectorStore instance from an
array of Document instances. It adds the documents to the store.
Array of Document instances.
Embeddings instance.
PGVectorStoreArgs instance.
Promise that resolves with a new instance of PGVectorStore.
Static fromStatic method to create a new PGVectorStore instance from an
array of texts and their metadata. It converts the texts into
Document instances and adds them to the store.
Array of texts.
Array of metadata objects or a single metadata object.
Embeddings instance.
PGVectorStoreArgs instance.
Promise that resolves with a new instance of PGVectorStore.
Static initializeStatic method to create a new PGVectorStore instance from a
connection. It creates a table if one does not exist, and calls
connect to return a new instance of PGVectorStore.
Embeddings instance.
A new instance of PGVectorStore.
Generated using TypeDoc
Class that provides an interface to a Postgres vector database. It extends the
VectorStorebase class and implements methods for adding documents and vectors, performing similarity searches, and ensuring the existence of a table in the database.